import pandas as pd
import numpy as np
import igraph as ig
import collections
import matplotlib
%matplotlib inline

Explore Data

df_unweighted = pd.read_csv('combined_final_unweighted.csv')
df_unweighted

	node_start	node_end	status
0	Hong Kong	Samoa	jurisdiction
1	Hong Kong	Samoa	jurisdiction
2	Hong Kong	Samoa	jurisdiction
3	Hong Kong	Samoa	jurisdiction
4	Hong Kong	Samoa	jurisdiction
...	...	...	...
338127	Indonesia	British Virgin Islands	officer_base
338128	Cook Islands	British Virgin Islands	officer_base
338129	Hong Kong	British Virgin Islands	officer_base
338130	Cook Islands	United Kingdom	officer_base
338131	Hong Kong	United Kingdom	officer_base

338132 rows × 3 columns

df_weighted = pd.read_csv('combined_final_weighted.csv')
df_weighted

	node_start	node_end	status	weight
0	Hong Kong	Samoa	jurisdiction	2.0
1	Hong Kong	Samoa	jurisdiction	2.0
2	Hong Kong	Samoa	jurisdiction	2.0
3	Hong Kong	Samoa	jurisdiction	2.0
4	Hong Kong	Samoa	jurisdiction	2.0
...	...	...	...	...
338119	Indonesia	British Virgin Islands	officer_base	1.0
338120	Cook Islands	British Virgin Islands	officer_base	1.0
338121	Hong Kong	British Virgin Islands	officer_base	1.0
338122	Cook Islands	United Kingdom	officer_base	1.0
338123	Hong Kong	United Kingdom	officer_base	1.0

338124 rows × 4 columns

df_weighted['status'].unique()

array(['jurisdiction', 'intermediary_base', 'intermediary_jurisdiction',
       'officer_base', 'officer_jurisdiction'], dtype=object)

df_weighted_jur = df_weighted[df_weighted['status'] == 'jurisdiction']
df_jur = df_weighted_jur.drop(['weight','status'], axis = 1)
df_jur

	node_start	node_end
0	Hong Kong	Samoa
1	Hong Kong	Samoa
2	Hong Kong	Samoa
3	Hong Kong	Samoa
4	Hong Kong	Samoa
...	...	...
216554	British Virgin Islands	Cook Islands
216555	British Virgin Islands	Cook Islands
216556	British Virgin Islands	Cook Islands
216557	British Virgin Islands	Cook Islands
216558	Vanuatu	Cook Islands

216559 rows × 2 columns

df_jur_grouped = df_jur.groupby(['node_start','node_end'], as_index = False).size()
df_jur_grouped

	node_start	node_end	size
0	Albania	Bahamas	2
1	Andorra	Bahamas	26
2	Andorra	British Virgin Islands	39
3	Andorra	Cayman Islands	1
4	Andorra	Costa Rica	3
...	...	...	...
1212	Vietnam	Panama	1
1213	Yemen	Panama	1
1214	Zambia	British Virgin Islands	2
1215	Zimbabwe	British Virgin Islands	5
1216	Zimbabwe	Panama	3

1217 rows × 3 columns

Convert Data to an Undirected Edge List

df_jur_grouped_list = df_jur_grouped.values.tolist()
df_jur_grouped_list

[['Albania', 'Bahamas', 2],
 ['Andorra', 'Bahamas', 26],
 ['Andorra', 'British Virgin Islands', 39],
 ['Andorra', 'Cayman Islands', 1],
 ['Andorra', 'Costa Rica', 3],
 ['Andorra', 'Hong Kong', 9],
 ['Andorra', 'Nevada', 24],
 ['Andorra', 'Niue', 12],
 ['Andorra', 'Panama', 279],
 ['Andorra', 'Samoa', 9],
 ['Andorra', 'Seychelles', 72],
 ['Andorra', 'United Kingdom', 1],
 ['Andorra', 'Uruguay', 15],
 ['Angola', 'Bermuda', 1],
 ['Angola', 'British Virgin Islands', 11],
 ['Antigua and Barbuda', 'Barbados', 8],
 ['Antigua and Barbuda', 'British Virgin Islands', 1],
 ['Antigua and Barbuda', 'Cayman Islands', 1],
 ['Antigua and Barbuda', 'Niue', 2],
 ['Antigua and Barbuda', 'Panama', 15],
 ['Antigua and Barbuda', 'Saint Kitts and Nevis', 1],
 ['Antigua and Barbuda', 'Seychelles', 3],
 ['Argentina', 'Bahamas', 23],
 ['Argentina', 'Barbados', 1],
 ['Argentina', 'British Virgin Islands', 83],
 ['Argentina', 'Cayman Islands', 3],
 ['Argentina', 'Costa Rica', 1],
 ['Argentina', 'Hong Kong', 4],
 ['Argentina', 'Nevada', 4],
 ['Argentina', 'New Zealand', 1],
 ['Argentina', 'Niue', 2],
 ['Argentina', 'Panama', 146],
 ['Argentina', 'Seychelles', 6],
 ['Aruba', 'Barbados', 1],
 ['Aruba', 'British Anguilla', 4],
 ['Aruba', 'British Virgin Islands', 19],
 ['Aruba', 'Panama', 4],
 ['Australia', 'Bahamas', 2],
 ['Australia', 'Barbados', 2],
 ['Australia', 'Bermuda', 42],
 ['Australia', 'British Anguilla', 1],
 ['Australia', 'British Virgin Islands', 75],
 ['Australia', 'Cayman Islands', 6],
 ['Australia', 'Cook Islands', 1],
 ['Australia', 'Isle of Man', 3],
 ['Australia', 'Niue', 4],
 ['Australia', 'Panama', 20],
 ['Australia', 'Saint Kitts and Nevis', 2],
 ['Australia', 'Seychelles', 4],
 ['Austria', 'Bahamas', 4],
 ['Austria', 'Bermuda', 4],
 ['Austria', 'British Anguilla', 1],
 ['Austria', 'British Virgin Islands', 7],
 ['Austria', 'Cayman Islands', 2],
 ['Austria', 'Panama', 12],
 ['Austria', 'Saint Kitts and Nevis', 2],
 ['Azerbaijan', 'Bahamas', 4],
 ['Azerbaijan', 'British Virgin Islands', 4],
 ['Azerbaijan', 'Saint Kitts and Nevis', 2],
 ['Bahamas', 'Barbados', 50],
 ['Bahamas', 'Bermuda', 9],
 ['Bahamas', 'British Anguilla', 45],
 ['Bahamas', 'British Virgin Islands', 1764],
 ['Bahamas', 'Cayman Islands', 23],
 ['Bahamas', 'Cook Islands', 1],
 ['Bahamas', 'Costa Rica', 9],
 ['Bahamas', 'Cyprus', 2],
 ['Bahamas', 'Hong Kong', 6],
 ['Bahamas', 'Malta', 1],
 ['Bahamas', 'Netherlands Antilles', 1],
 ['Bahamas', 'Nevada', 291],
 ['Bahamas', 'Niue', 58],
 ['Bahamas', 'Panama', 2256],
 ['Bahamas', 'Samoa', 27],
 ['Bahamas', 'Seychelles', 105],
 ['Bahamas', 'United Kingdom', 4],
 ['Bahamas', 'Uruguay', 2],
 ['Bahamas', 'Wyoming', 15],
 ['Bahrain', 'Bermuda', 3],
 ['Bahrain', 'British Virgin Islands', 10],
 ['Bahrain', 'Panama', 3],
 ['Bahrain', 'United Arab Emirates', 1],
 ['Bangladesh', 'Bermuda', 1],
 ['Bangladesh', 'British Virgin Islands', 2],
 ['Barbados', 'Bahamas', 2],
 ['Barbados', 'Bermuda', 8],
 ['Barbados', 'British Virgin Islands', 15],
 ['Barbados', 'Cayman Islands', 4],
 ['Barbados', 'Panama', 18],
 ['Belarus', 'British Virgin Islands', 14],
 ['Belarus', 'Hong Kong', 1],
 ['Belarus', 'Panama', 1],
 ['Belgium', 'Bahamas', 8],
 ['Belgium', 'Barbados', 1],
 ['Belgium', 'Bermuda', 1],
 ['Belgium', 'British Anguilla', 1],
 ['Belgium', 'British Virgin Islands', 18],
 ['Belgium', 'Isle of Man', 1],
 ['Belgium', 'Niue', 1],
 ['Belgium', 'Panama', 25],
 ['Belgium', 'Saint Kitts and Nevis', 1],
 ['Belgium', 'Samoa', 1],
 ['Belgium', 'Seychelles', 5],
 ['Belize', 'Bahamas', 250],
 ['Belize', 'Barbados', 3],
 ['Belize', 'British Anguilla', 19],
 ['Belize', 'British Virgin Islands', 166],
 ['Belize', 'Cayman Islands', 1],
 ['Belize', 'Hong Kong', 44],
 ['Belize', 'Mauritius', 1],
 ['Belize', 'Niue', 228],
 ['Belize', 'Panama', 423],
 ['Belize', 'Saint Kitts and Nevis', 1],
 ['Belize', 'Samoa', 45],
 ['Belize', 'Seychelles', 175],
 ['Belize', 'Wyoming', 1],
 ['Bermuda', 'Bahamas', 21],
 ['Bermuda', 'Barbados', 37],
 ['Bermuda', 'British Virgin Islands', 138],
 ['Bermuda', 'Cayman Islands', 17],
 ['Bermuda', 'Cook Islands', 1],
 ['Bermuda', 'Cyprus', 1],
 ['Bermuda', 'Jersey', 3],
 ['Bermuda', 'Liberia', 4],
 ['Bermuda', 'Liechtenstein', 5],
 ['Bermuda', 'Netherlands', 5],
 ['Bermuda', 'Netherlands Antilles', 1],
 ['Bermuda', 'Panama', 21],
 ['Bermuda', 'Saint Kitts and Nevis', 1],
 ['Bermuda', 'Singapore', 1],
 ['Bermuda', 'Turks and Caicos Islands', 1],
 ['Bermuda', 'United States', 21],
 ['Bolivia', 'British Virgin Islands', 2],
 ['Bolivia', 'Hong Kong', 2],
 ['Bolivia', 'Panama', 91],
 ['Botswana', 'British Virgin Islands', 2],
 ['Botswana', 'Panama', 1],
 ['Botswana', 'Seychelles', 3],
 ['Brazil', 'Bahamas', 33],
 ['Brazil', 'Barbados', 2],
 ['Brazil', 'Bermuda', 4],
 ['Brazil', 'British Anguilla', 14],
 ['Brazil', 'British Virgin Islands', 473],
 ['Brazil', 'Cayman Islands', 21],
 ['Brazil', 'Hong Kong', 2],
 ['Brazil', 'Isle of Man', 3],
 ['Brazil', 'Nevada', 132],
 ['Brazil', 'New Zealand', 1],
 ['Brazil', 'Niue', 91],
 ['Brazil', 'Panama', 521],
 ['Brazil', 'Samoa', 43],
 ['Brazil', 'Seychelles', 79],
 ['Brazil', 'United Kingdom', 6],
 ['Brazil', 'Uruguay', 1],
 ['Brazil', 'Wyoming', 4],
 ['British Anguilla', 'Barbados', 7],
 ['British Anguilla', 'British Anguilla', 21],
 ['British Anguilla', 'British Virgin Islands', 2],
 ['British Virgin Islands', 'Bahamas', 145],
 ['British Virgin Islands', 'Barbados', 1053],
 ['British Virgin Islands', 'Belize', 1],
 ['British Virgin Islands', 'Bermuda', 13],
 ['British Virgin Islands', 'British Anguilla', 1],
 ['British Virgin Islands', 'Cayman Islands', 9],
 ['British Virgin Islands', 'Cook Islands', 75],
 ['British Virgin Islands', 'Hong Kong', 3],
 ['British Virgin Islands', 'Isle of Man', 2],
 ['British Virgin Islands', 'Nevada', 2],
 ['British Virgin Islands', 'Niue', 2],
 ['British Virgin Islands', 'Panama', 92],
 ['British Virgin Islands', 'Saint Kitts and Nevis', 2],
 ['British Virgin Islands', 'Seychelles', 11],
 ['British Virgin Islands', 'State of Delaware', 1],
 ['Brunei', 'British Virgin Islands', 3],
 ['Bulgaria', 'Bahamas', 2],
 ['Bulgaria', 'British Virgin Islands', 20],
 ['Bulgaria', 'Niue', 1],
 ['Bulgaria', 'Panama', 5],
 ['Bulgaria', 'Seychelles', 22],
 ['Cameroon', 'Seychelles', 1],
 ['Canada', 'Bahamas', 36],
 ['Canada', 'Barbados', 132],
 ['Canada', 'Bermuda', 79],
 ['Canada', 'British Anguilla', 2],
 ['Canada', 'British Virgin Islands', 198],
 ['Canada', 'Cayman Islands', 64],
 ['Canada', 'Hong Kong', 1],
 ['Canada', 'Isle of Man', 5],
 ['Canada', 'Nevada', 2],
 ['Canada', 'Niue', 31],
 ['Canada', 'Panama', 74],
 ['Canada', 'Saint Kitts and Nevis', 1],
 ['Canada', 'Seychelles', 17],
 ['Canada', 'Singapore', 1],
 ['Cayman Islands', 'Antigua and Barbuda', 3],
 ['Cayman Islands', 'Aruba', 2],
 ['Cayman Islands', 'Bahamas', 43],
 ['Cayman Islands', 'Barbados', 113],
 ['Cayman Islands', 'Bermuda', 21],
 ['Cayman Islands', 'British Virgin Islands', 1057],
 ['Cayman Islands', 'Cook Islands', 36],
 ['Cayman Islands', 'Cyprus', 3],
 ['Cayman Islands', 'Gibraltar', 1],
 ['Cayman Islands', 'Grenada', 1],
 ['Cayman Islands', 'Guernsey', 9],
 ['Cayman Islands', 'Hong Kong', 76],
 ['Cayman Islands', 'Isle of Man', 8],
 ['Cayman Islands', 'Jersey', 2],
 ['Cayman Islands', 'Liberia', 1],
 ['Cayman Islands', 'Liechtenstein', 1],
 ['Cayman Islands', 'Luxembourg', 1],
 ['Cayman Islands', 'Malta', 1],
 ['Cayman Islands', 'Marshall Islands', 1],
 ['Cayman Islands', 'Netherlands', 1],
 ['Cayman Islands', 'Netherlands Antilles', 2],
 ['Cayman Islands', 'New Zealand', 2],
 ['Cayman Islands', 'Niue', 1],
 ['Cayman Islands', 'Panama', 53],
 ['Cayman Islands', 'Saint Kitts and Nevis', 96],
 ['Cayman Islands', 'Saint Vincent and the Grenadines', 12],
 ['Cayman Islands', 'Samoa', 853],
 ['Cayman Islands', 'Singapore', 4],
 ['Cayman Islands', 'State of Delaware', 116],
 ['Cayman Islands', 'Turks and Caicos Islands', 1],
 ['Cayman Islands', 'US Virgin Islands', 1],
 ['Cayman Islands', 'United States', 22],
 ['Central African Republic', 'British Virgin Islands', 1],
 ['Chad', 'British Virgin Islands', 10],
 ['Chad', 'Panama', 1],
 ['Chile', 'Bahamas', 14],
 ['Chile', 'Barbados', 1],
 ['Chile', 'Bermuda', 1],
 ['Chile', 'British Anguilla', 2],
 ['Chile', 'British Virgin Islands', 31],
 ['Chile', 'Costa Rica', 1],
 ['Chile', 'Nevada', 6],
 ['Chile', 'Panama', 83],
 ['Chile', 'Seychelles', 2],
 ['Chile', 'Wyoming', 1],
 ['China', 'Bahamas', 5],
 ['China', 'Barbados', 1],
 ['China', 'Belize', 1],
 ['China', 'Bermuda', 77],
 ['China', 'British Anguilla', 148],
 ['China', 'British Virgin Islands', 2290],
 ['China', 'Cayman Islands', 147],
 ['China', 'Hong Kong', 9],
 ['China', 'Isle of Man', 1],
 ['China', 'Nevada', 9],
 ['China', 'Niue', 12],
 ['China', 'Panama', 13],
 ['China', 'Saint Kitts and Nevis', 2],
 ['China', 'Samoa', 363],
 ['China', 'Seychelles', 625],
 ['Colombia', 'Bahamas', 22],
 ['Colombia', 'Belize', 19],
 ['Colombia', 'Bermuda', 2],
 ['Colombia', 'British Anguilla', 59],
 ['Colombia', 'British Virgin Islands', 265],
 ['Colombia', 'Cayman Islands', 3],
 ['Colombia', 'Costa Rica', 42],
 ['Colombia', 'Cyprus', 1],
 ['Colombia', 'Hong Kong', 1],
 ['Colombia', 'Malta', 1],
 ['Colombia', 'Nevada', 10],
 ['Colombia', 'New Zealand', 3],
 ['Colombia', 'Niue', 2],
 ['Colombia', 'Panama', 1357],
 ['Colombia', 'Saint Kitts and Nevis', 3],
 ['Colombia', 'Samoa', 6],
 ['Colombia', 'Seychelles', 18],
 ['Colombia', 'United Kingdom', 51],
 ['Colombia', 'Uruguay', 1],
 ['Cook Islands', 'Bermuda', 1],
 ['Cook Islands', 'British Virgin Islands', 4],
 ['Cook Islands', 'Hong Kong', 1],
 ['Cook Islands', 'Niue', 1],
 ['Cook Islands', 'Samoa', 1],
 ['Costa Rica', 'Bahamas', 32],
 ['Costa Rica', 'British Anguilla', 11],
 ['Costa Rica', 'British Virgin Islands', 141],
 ['Costa Rica', 'Cayman Islands', 2],
 ['Costa Rica', 'Hong Kong', 5],
 ['Costa Rica', 'Isle of Man', 1],
 ['Costa Rica', 'Nevada', 2],
 ['Costa Rica', 'Niue', 4],
 ['Costa Rica', 'Panama', 686],
 ['Costa Rica', 'Samoa', 2],
 ['Costa Rica', 'Seychelles', 2],
 ['Costa Rica', 'United Kingdom', 1],
 ['Croatia', 'Barbados', 1],
 ['Croatia', 'British Virgin Islands', 2],
 ['Croatia', 'Panama', 1],
 ['Cuba', 'Barbados', 1],
 ['Cuba', 'British Virgin Islands', 8],
 ['Cuba', 'Cayman Islands', 1],
 ['Cuba', 'Panama', 3],
 ['Curaçao', 'Barbados', 19],
 ['Curaçao', 'Bermuda', 1],
 ['Curaçao', 'British Virgin Islands', 10],
 ['Curaçao', 'Panama', 8],
 ['Curaçao', 'Seychelles', 12],
 ['Cyprus', 'Bahamas', 649],
 ['Cyprus', 'Barbados', 5],
 ['Cyprus', 'Belize', 11],
 ['Cyprus', 'Bermuda', 8],
 ['Cyprus', 'British Anguilla', 5],
 ['Cyprus', 'British Virgin Islands', 1869],
 ['Cyprus', 'Cayman Islands', 10],
 ['Cyprus', 'Hong Kong', 4],
 ['Cyprus', 'Isle of Man', 4],
 ['Cyprus', 'Mauritius', 1],
 ['Cyprus', 'Nevada', 1],
 ['Cyprus', 'Niue', 313],
 ['Cyprus', 'Panama', 305],
 ['Cyprus', 'Saint Kitts and Nevis', 3],
 ['Cyprus', 'Samoa', 5],
 ['Cyprus', 'Seychelles', 448],
 ['Czech Republic', 'Bahamas', 2],
 ['Czech Republic', 'Barbados', 1],
 ['Czech Republic', 'British Virgin Islands', 25],
 ['Czech Republic', 'Isle of Man', 1],
 ['Czech Republic', 'Panama', 6],
 ['Czech Republic', 'Samoa', 1],
 ['Czech Republic', 'Seychelles', 1],
 ["Côte d'Ivoire", 'British Virgin Islands', 10],
 ["Côte d'Ivoire", 'Nevada', 1],
 ["Côte d'Ivoire", 'Niue', 1],
 ["Côte d'Ivoire", 'Panama', 7],
 ["Côte d'Ivoire", 'Samoa', 2],
 ['Denmark', 'Bahamas', 4],
 ['Denmark', 'Barbados', 1],
 ['Denmark', 'Bermuda', 2],
 ['Denmark', 'British Virgin Islands', 4],
 ['Denmark', 'Panama', 4],
 ['Denmark', 'Seychelles', 2],
 ['Djibouti', 'Panama', 1],
 ['Dominica', 'Barbados', 1],
 ['Dominica', 'British Virgin Islands', 4],
 ['Dominica', 'Panama', 62],
 ['Dominican Republic', 'Bahamas', 8],
 ['Dominican Republic', 'Barbados', 3],
 ['Dominican Republic', 'British Anguilla', 24],
 ['Dominican Republic', 'British Virgin Islands', 132],
 ['Dominican Republic', 'Cayman Islands', 5],
 ['Dominican Republic', 'Panama', 312],
 ['Dominican Republic', 'Samoa', 1],
 ['Dominican Republic', 'Seychelles', 11],
 ['Ecuador', 'Bahamas', 49],
 ['Ecuador', 'Barbados', 2],
 ['Ecuador', 'Belize', 5],
 ['Ecuador', 'Bermuda', 1],
 ['Ecuador', 'British Anguilla', 146],
 ['Ecuador', 'British Virgin Islands', 252],
 ['Ecuador', 'Cayman Islands', 1],
 ['Ecuador', 'Costa Rica', 8],
 ['Ecuador', 'Hong Kong', 2],
 ['Ecuador', 'Nevada', 199],
 ['Ecuador', 'New Zealand', 9],
 ['Ecuador', 'Niue', 26],
 ['Ecuador', 'Panama', 1101],
 ['Ecuador', 'Samoa', 20],
 ['Ecuador', 'Seychelles', 26],
 ['Ecuador', 'United Kingdom', 7],
 ['Ecuador', 'Uruguay', 2],
 ['Egypt', 'Bahamas', 5],
 ['Egypt', 'Barbados', 1],
 ['Egypt', 'Bermuda', 3],
 ['Egypt', 'British Virgin Islands', 17],
 ['Egypt', 'Cayman Islands', 1],
 ['Egypt', 'Panama', 20],
 ['Egypt', 'Seychelles', 1],
 ['El Salvador', 'Bahamas', 6],
 ['El Salvador', 'Barbados', 2],
 ['El Salvador', 'British Anguilla', 1],
 ['El Salvador', 'British Virgin Islands', 19],
 ['El Salvador', 'Nevada', 7],
 ['El Salvador', 'Niue', 1],
 ['El Salvador', 'Panama', 195],
 ['El Salvador', 'Seychelles', 5],
 ['Estonia', 'Bahamas', 114],
 ['Estonia', 'British Anguilla', 1],
 ['Estonia', 'British Virgin Islands', 644],
 ['Estonia', 'Cyprus', 1],
 ['Estonia', 'Isle of Man', 1],
 ['Estonia', 'Malta', 1],
 ['Estonia', 'Niue', 68],
 ['Estonia', 'Panama', 14],
 ['Estonia', 'Samoa', 3],
 ['Estonia', 'Seychelles', 34],
 ['Fiji', 'Cook Islands', 1],
 ['Finland', 'Bahamas', 32],
 ['Finland', 'British Virgin Islands', 25],
 ['Finland', 'Cayman Islands', 7],
 ['Finland', 'Niue', 1],
 ['Finland', 'Panama', 3],
 ['Finland', 'Seychelles', 4],
 ['France', 'Bahamas', 23],
 ['France', 'Barbados', 2],
 ['France', 'Bermuda', 25],
 ['France', 'British Anguilla', 10],
 ['France', 'British Virgin Islands', 138],
 ['France', 'Cayman Islands', 6],
 ['France', 'Cook Islands', 1],
 ['France', 'Hong Kong', 2],
 ['France', 'Isle of Man', 1],
 ['France', 'Nevada', 1],
 ['France', 'Niue', 18],
 ['France', 'Panama', 78],
 ['France', 'Samoa', 3],
 ['France', 'Seychelles', 14],
 ['French Guiana', 'British Virgin Islands', 20],
 ['French Guiana', 'Cayman Islands', 8],
 ['French Guiana', 'Guernsey', 2],
 ['Georgia', 'British Anguilla', 1],
 ['Germany', 'Bahamas', 1],
 ['Germany', 'Barbados', 3],
 ['Germany', 'Bermuda', 4],
 ['Germany', 'British Anguilla', 1],
 ['Germany', 'British Virgin Islands', 35],
 ['Germany', 'Cayman Islands', 5],
 ['Germany', 'Cyprus', 1],
 ['Germany', 'Isle of Man', 1],
 ['Germany', 'Niue', 2],
 ['Germany', 'Panama', 129],
 ['Germany', 'Seychelles', 8],
 ['Ghana', 'Bahamas', 1],
 ['Ghana', 'British Anguilla', 1],
 ['Ghana', 'British Virgin Islands', 1],
 ['Gibraltar', 'Bahamas', 150],
 ['Gibraltar', 'Barbados', 8],
 ['Gibraltar', 'Belize', 2],
 ['Gibraltar', 'British Virgin Islands', 1584],
 ['Gibraltar', 'Cayman Islands', 1],
 ['Gibraltar', 'Isle of Man', 4],
 ['Gibraltar', 'Malta', 1],
 ['Gibraltar', 'Nevada', 2],
 ['Gibraltar', 'Niue', 28],
 ['Gibraltar', 'Panama', 195],
 ['Gibraltar', 'Samoa', 14],
 ['Gibraltar', 'Seychelles', 64],
 ['Greece', 'Bahamas', 7],
 ['Greece', 'Bermuda', 5],
 ['Greece', 'British Virgin Islands', 62],
 ['Greece', 'Cayman Islands', 1],
 ['Greece', 'Liberia', 1],
 ['Greece', 'Panama', 134],
 ['Greece', 'Seychelles', 21],
 ['Grenada', 'Barbados', 2],
 ['Grenada', 'Saint Kitts and Nevis', 4],
 ['Guam', 'British Virgin Islands', 1],
 ['Guatemala', 'Bahamas', 39],
 ['Guatemala', 'British Anguilla', 23],
 ['Guatemala', 'British Virgin Islands', 185],
 ['Guatemala', 'Cayman Islands', 8],
 ['Guatemala', 'Niue', 140],
 ['Guatemala', 'Panama', 801],
 ['Guatemala', 'Saint Kitts and Nevis', 4],
 ['Guatemala', 'Samoa', 16],
 ['Guatemala', 'Seychelles', 30],
 ['Guernsey', 'Bahamas', 1504],
 ['Guernsey', 'Barbados', 13],
 ['Guernsey', 'Bermuda', 3],
 ['Guernsey', 'British Anguilla', 27],
 ['Guernsey', 'British Virgin Islands', 4734],
 ['Guernsey', 'Cayman Islands', 4],
 ['Guernsey', 'Cook Islands', 2],
 ['Guernsey', 'Cyprus', 1],
 ['Guernsey', 'Hong Kong', 2],
 ['Guernsey', 'Isle of Man', 22],
 ['Guernsey', 'Jersey', 8],
 ['Guernsey', 'Niue', 302],
 ['Guernsey', 'Panama', 705],
 ['Guernsey', 'Samoa', 19],
 ['Guernsey', 'Seychelles', 52],
 ['Guernsey', 'Turks and Caicos Islands', 1],
 ['Guernsey', 'Uruguay', 1],
 ['Guyana', 'Barbados', 2],
 ['Haiti', 'British Virgin Islands', 3],
 ['Haiti', 'Nevada', 5],
 ['Honduras', 'Bahamas', 1],
 ['Honduras', 'British Virgin Islands', 4],
 ['Honduras', 'Cayman Islands', 1],
 ['Honduras', 'Panama', 19],
 ['Hong Kong', 'Bahamas', 960],
 ['Hong Kong', 'Barbados', 5],
 ['Hong Kong', 'Belize', 6],
 ['Hong Kong', 'Bermuda', 393],
 ['Hong Kong', 'British Anguilla', 1366],
 ['Hong Kong', 'British Virgin Islands', 27498],
 ['Hong Kong', 'Cayman Islands', 229],
 ['Hong Kong', 'Cook Islands', 12],
 ['Hong Kong', 'Isle of Man', 10],
 ['Hong Kong', 'Labuan', 2],
 ['Hong Kong', 'Netherlands', 1],
 ['Hong Kong', 'Nevada', 43],
 ['Hong Kong', 'Niue', 1055],
 ['Hong Kong', 'Panama', 609],
 ['Hong Kong', 'Samoa', 3012],
 ['Hong Kong', 'Seychelles', 3749],
 ['Hong Kong', 'Singapore', 6],
 ['Hong Kong', 'Thailand', 1],
 ['Hungary', 'Bahamas', 4],
 ['Hungary', 'Barbados', 3],
 ['Hungary', 'British Virgin Islands', 25],
 ['Hungary', 'Nevada', 1],
 ['Hungary', 'Niue', 25],
 ['Hungary', 'Panama', 7],
 ['Hungary', 'Samoa', 1],
 ['Hungary', 'Seychelles', 27],
 ['Iceland', 'Bermuda', 2],
 ['Iceland', 'British Virgin Islands', 14],
 ['Iceland', 'Panama', 1],
 ['India', 'Barbados', 1],
 ['India', 'Bermuda', 10],
 ['India', 'British Virgin Islands', 14],
 ['India', 'Cayman Islands', 2],
 ['India', 'Isle of Man', 8],
 ['India', 'Niue', 1],
 ['India', 'Panama', 2],
 ['India', 'Seychelles', 6],
 ['India', 'Singapore', 1],
 ['Indonesia', 'Bermuda', 5],
 ['Indonesia', 'British Virgin Islands', 51],
 ['Indonesia', 'Cayman Islands', 10],
 ['Indonesia', 'Cook Islands', 6],
 ['Indonesia', 'Panama', 1],
 ['Indonesia', 'Samoa', 2],
 ['Indonesia', 'Seychelles', 2],
 ['Indonesia', 'Singapore', 4],
 ['Iran', 'Panama', 4],
 ['Ireland', 'Bahamas', 25],
 ['Ireland', 'Barbados', 13],
 ['Ireland', 'Bermuda', 20],
 ['Ireland', 'British Anguilla', 1],
 ['Ireland', 'British Virgin Islands', 186],
 ['Ireland', 'Cayman Islands', 9],
 ['Ireland', 'Guernsey', 2],
 ['Ireland', 'Hong Kong', 1],
 ['Ireland', 'Isle of Man', 30],
 ['Ireland', 'Jersey', 1],
 ['Ireland', 'Niue', 31],
 ['Ireland', 'Panama', 56],
 ['Ireland', 'Samoa', 11],
 ['Ireland', 'Seychelles', 13],
 ['Isle of Man', 'Bahamas', 885],
 ['Isle of Man', 'Barbados', 26],
 ['Isle of Man', 'Bermuda', 5],
 ['Isle of Man', 'British Anguilla', 35],
 ['Isle of Man', 'British Virgin Islands', 3064],
 ['Isle of Man', 'Cayman Islands', 12],
 ['Isle of Man', 'Cyprus', 7],
 ['Isle of Man', 'Guernsey', 4],
 ['Isle of Man', 'Hong Kong', 7],
 ['Isle of Man', 'Isle of Man', 7],
 ['Isle of Man', 'Jersey', 9],
 ['Isle of Man', 'Malta', 1],
 ['Isle of Man', 'Nevada', 7],
 ['Isle of Man', 'Niue', 271],
 ['Isle of Man', 'Panama', 500],
 ['Isle of Man', 'Samoa', 70],
 ['Isle of Man', 'Seychelles', 80],
 ['Isle of Man', 'United Arab Emirates', 1],
 ['Isle of Man', 'United Kingdom', 2],
 ['Isle of Man', 'Uruguay', 1],
 ['Israel', 'Bahamas', 16],
 ['Israel', 'Barbados', 1],
 ['Israel', 'Belize', 10],
 ['Israel', 'Bermuda', 1],
 ['Israel', 'British Anguilla', 150],
 ['Israel', 'British Virgin Islands', 174],
 ['Israel', 'Cayman Islands', 4],
 ['Israel', 'Cyprus', 4],
 ['Israel', 'Hong Kong', 1],
 ['Israel', 'Isle of Man', 2],
 ['Israel', 'Malta', 4],
 ['Israel', 'Niue', 11],
 ['Israel', 'Panama', 127],
 ['Israel', 'Samoa', 47],
 ['Israel', 'Seychelles', 122],
 ['Italy', 'Bahamas', 3],
 ['Italy', 'British Anguilla', 204],
 ['Italy', 'British Virgin Islands', 31],
 ['Italy', 'Cayman Islands', 2],
 ['Italy', 'Cyprus', 13],
 ['Italy', 'Hong Kong', 1],
 ['Italy', 'Isle of Man', 2],
 ['Italy', 'Nevada', 7],
 ['Italy', 'Niue', 22],
 ['Italy', 'Panama', 40],
 ['Italy', 'Samoa', 3],
 ['Italy', 'Seychelles', 22],
 ['Jamaica', 'Bahamas', 1],
 ['Jamaica', 'Barbados', 12],
 ['Jamaica', 'British Virgin Islands', 4],
 ['Jamaica', 'Cayman Islands', 5],
 ['Japan', 'Bermuda', 8],
 ['Japan', 'British Virgin Islands', 15],
 ['Japan', 'Cayman Islands', 16],
 ['Japan', 'Cook Islands', 1],
 ['Japan', 'Niue', 2],
 ['Japan', 'Panama', 7],
 ['Japan', 'Seychelles', 1],
 ['Japan', 'Singapore', 1],
 ['Jersey', 'Bahamas', 1244],
 ['Jersey', 'Barbados', 44],
 ['Jersey', 'Bermuda', 13],
 ['Jersey', 'British Anguilla', 2],
 ['Jersey', 'British Virgin Islands', 10602],
 ['Jersey', 'Cayman Islands', 10],
 ['Jersey', 'Cook Islands', 2],
 ['Jersey', 'Cyprus', 1],
 ['Jersey', 'Hong Kong', 66],
 ['Jersey', 'Isle of Man', 19],
 ['Jersey', 'Malta', 4],
 ['Jersey', 'Mauritius', 1],
 ['Jersey', 'Nevada', 3],
 ['Jersey', 'Niue', 344],
 ['Jersey', 'Panama', 2129],
 ['Jersey', 'Saint Kitts and Nevis', 1],
 ['Jersey', 'Samoa', 17],
 ['Jersey', 'Seychelles', 18],
 ['Jersey', 'Singapore', 1],
 ['Jersey', 'United Kingdom', 6],
 ['Jersey', 'Wyoming', 1],
 ['Jordan', 'Bahamas', 14],
 ['Jordan', 'Bermuda', 1],
 ['Jordan', 'British Virgin Islands', 1146],
 ['Jordan', 'Panama', 394],
 ['Jordan', 'Seychelles', 1],
 ['Kazakhstan', 'Bermuda', 1],
 ['Kazakhstan', 'British Virgin Islands', 8],
 ['Kenya', 'Bahamas', 1],
 ['Kenya', 'British Anguilla', 2],
 ['Kenya', 'British Virgin Islands', 15],
 ['Kenya', 'Cayman Islands', 1],
 ['Kenya', 'Isle of Man', 1],
 ['Kenya', 'Panama', 6],
 ['Kenya', 'Seychelles', 1],
 ['Kuwait', 'Bahamas', 2],
 ['Kuwait', 'Bermuda', 1],
 ['Kuwait', 'British Virgin Islands', 9],
 ['Kuwait', 'Cayman Islands', 4],
 ['Kuwait', 'Panama', 36],
 ['Kuwait', 'Seychelles', 13],
 ['Kyrgyzstan', 'Isle of Man', 1],
 ['Latvia', 'Bahamas', 51],
 ['Latvia', 'British Virgin Islands', 1303],
 ['Latvia', 'Hong Kong', 1],
 ['Latvia', 'Niue', 13],
 ['Latvia', 'Panama', 12],
 ['Latvia', 'Seychelles', 1],
 ['Lebanon', 'Bahamas', 7],
 ['Lebanon', 'Bermuda', 1],
 ['Lebanon', 'British Virgin Islands', 213],
 ['Lebanon', 'Cayman Islands', 1],
 ['Lebanon', 'Hong Kong', 3],
 ['Lebanon', 'Niue', 46],
 ['Lebanon', 'Panama', 121],
 ['Lebanon', 'Samoa', 12],
 ['Lebanon', 'Seychelles', 88],
 ['Lesotho', 'Seychelles', 1],
 ['Liberia', 'Barbados', 19],
 ['Liberia', 'Bermuda', 4],
 ['Liberia', 'Liechtenstein', 36],
 ['Liberia', 'Panama', 2],
 ['Libya', 'British Virgin Islands', 1],
 ['Libya', 'Panama', 1],
 ['Liechtenstein', 'Bahamas', 87],
 ['Liechtenstein', 'Barbados', 5],
 ['Liechtenstein', 'Bermuda', 4],
 ['Liechtenstein', 'British Anguilla', 5],
 ['Liechtenstein', 'British Virgin Islands', 1268],
 ['Liechtenstein', 'Cayman Islands', 3],
 ['Liechtenstein', 'Cyprus', 1],
 ['Liechtenstein', 'Hong Kong', 11],
 ['Liechtenstein', 'Niue', 49],
 ['Liechtenstein', 'Panama', 444],
 ['Liechtenstein', 'Samoa', 32],
 ['Liechtenstein', 'Seychelles', 169],
 ['Liechtenstein', 'Singapore', 3],
 ['Lithuania', 'Bahamas', 2],
 ['Lithuania', 'British Anguilla', 1],
 ['Lithuania', 'British Virgin Islands', 4],
 ['Lithuania', 'Niue', 20],
 ['Lithuania', 'Panama', 6],
 ['Luxembourg', 'Bahamas', 423],
 ['Luxembourg', 'Barbados', 11],
 ['Luxembourg', 'Belize', 8],
 ['Luxembourg', 'Bermuda', 4],
 ['Luxembourg', 'British Anguilla', 7],
 ['Luxembourg', 'British Virgin Islands', 3082],
 ['Luxembourg', 'Cayman Islands', 8],
 ['Luxembourg', 'Costa Rica', 1],
 ['Luxembourg', 'Cyprus', 6],
 ['Luxembourg', 'Hong Kong', 39],
 ['Luxembourg', 'Malta', 6],
 ['Luxembourg', 'Nevada', 29],
 ['Luxembourg', 'Niue', 219],
 ['Luxembourg', 'Panama', 5584],
 ['Luxembourg', 'Samoa', 13],
 ['Luxembourg', 'Seychelles', 1421],
 ['Luxembourg', 'United Kingdom', 1],
 ['Luxembourg', 'Uruguay', 1],
 ['Luxembourg', 'Wyoming', 1],
 ['Macao', 'Bermuda', 2],
 ['Macao', 'British Virgin Islands', 24],
 ['Macao', 'Malta', 115],
 ['Macao', 'Seychelles', 1],
 ['Macedonia', 'British Virgin Islands', 1],
 ['Macedonia', 'Seychelles', 1],
 ['Malawi', 'British Virgin Islands', 1],
 ['Malaysia', 'Bahamas', 1],
 ['Malaysia', 'Barbados', 1],
 ['Malaysia', 'Bermuda', 17],
 ['Malaysia', 'British Anguilla', 1],
 ['Malaysia', 'British Virgin Islands', 42],
 ['Malaysia', 'Cayman Islands', 1],
 ['Malaysia', 'Cook Islands', 1],
 ['Malaysia', 'Labuan', 422],
 ['Malaysia', 'Niue', 4],
 ['Malaysia', 'Panama', 2],
 ['Malaysia', 'Samoa', 1],
 ['Malaysia', 'Seychelles', 1],
 ['Malaysia', 'Singapore', 2],
 ['Mali', 'British Virgin Islands', 8],
 ['Malta', 'Bahamas', 11],
 ['Malta', 'Barbados', 1],
 ['Malta', 'British Virgin Islands', 462],
 ['Malta', 'Hong Kong', 1],
 ['Malta', 'Isle of Man', 1],
 ['Malta', 'New Zealand', 9],
 ['Malta', 'Niue', 55],
 ['Malta', 'Panama', 107],
 ['Malta', 'Seychelles', 31],
 ['Marshall Islands', 'Barbados', 1],
 ['Marshall Islands', 'Cayman Islands', 1],
 ['Marshall Islands', 'Isle of Man', 1],
 ['Mauritius', 'Bahamas', 136],
 ['Mauritius', 'Barbados', 3],
 ['Mauritius', 'Bermuda', 2],
 ['Mauritius', 'British Virgin Islands', 920],
 ['Mauritius', 'Cayman Islands', 2],
 ['Mauritius', 'Isle of Man', 1],
 ['Mauritius', 'Jersey', 3],
 ['Mauritius', 'Niue', 32],
 ['Mauritius', 'Panama', 108],
 ['Mauritius', 'Samoa', 9],
 ['Mauritius', 'Seychelles', 15],
 ['Mexico', 'Bahamas', 3],
 ['Mexico', 'Barbados', 3],
 ['Mexico', 'Bermuda', 1],
 ['Mexico', 'British Virgin Islands', 14],
 ['Mexico', 'Cayman Islands', 5],
 ['Mexico', 'Hong Kong', 1],
 ['Mexico', 'Nevada', 1],
 ['Mexico', 'Panama', 39],
 ['Mexico', 'Seychelles', 6],
 ['Mexico', 'United Kingdom', 1],
 ['Moldova', 'British Virgin Islands', 2],
 ['Monaco', 'Bahamas', 83],
 ['Monaco', 'Belize', 2],
 ['Monaco', 'Bermuda', 32],
 ['Monaco', 'British Anguilla', 4],
 ['Monaco', 'British Virgin Islands', 1039],
 ['Monaco', 'Cayman Islands', 8],
 ['Monaco', 'Hong Kong', 1],
 ['Monaco', 'Isle of Man', 2],
 ['Monaco', 'Nevada', 6],
 ['Monaco', 'Niue', 49],
 ['Monaco', 'Panama', 1402],
 ['Monaco', 'Samoa', 20],
 ['Monaco', 'Seychelles', 566],
 ['Mongolia', 'British Virgin Islands', 3],
 ['Montenegro', 'British Virgin Islands', 12],
 ['Montenegro', 'Panama', 1],
 ['Morocco', 'British Virgin Islands', 21],
 ['Morocco', 'Panama', 2],
 ['Morocco', 'Seychelles', 18],
 ['Mozambique', 'British Virgin Islands', 1],
 ['Mozambique', 'Seychelles', 2],
 ['Namibia', 'Seychelles', 2],
 ['Nauru', 'Panama', 1],
 ['Netherlands', 'Bahamas', 28],
 ['Netherlands', 'Barbados', 18],
 ['Netherlands', 'Bermuda', 11],
 ['Netherlands', 'British Anguilla', 4],
 ['Netherlands', 'British Virgin Islands', 81],
 ['Netherlands', 'Cayman Islands', 6],
 ['Netherlands', 'Isle of Man', 2],
 ['Netherlands', 'Netherlands Antilles', 1],
 ['Netherlands', 'Niue', 7],
 ['Netherlands', 'Panama', 53],
 ['Netherlands', 'Samoa', 3],
 ['Netherlands', 'Seychelles', 6],
 ['Netherlands', 'Singapore', 1],
 ['New Zealand', 'Bahamas', 7],
 ['New Zealand', 'Bermuda', 18],
 ['New Zealand', 'British Virgin Islands', 19],
 ['New Zealand', 'Cook Islands', 1],
 ['New Zealand', 'Hong Kong', 1],
 ['New Zealand', 'Nevada', 1],
 ['New Zealand', 'Niue', 13],
 ['New Zealand', 'Panama', 10],
 ['New Zealand', 'Samoa', 3],
 ['Nicaragua', 'British Virgin Islands', 2],
 ['Nicaragua', 'Cayman Islands', 1],
 ['Nicaragua', 'Panama', 10],
 ['Nigeria', 'Bahamas', 5],
 ['Nigeria', 'Barbados', 1],
 ['Nigeria', 'Bermuda', 1],
 ['Nigeria', 'British Virgin Islands', 12],
 ['Nigeria', 'Cayman Islands', 2],
 ['Nigeria', 'Panama', 2],
 ['Nigeria', 'Seychelles', 2],
 ['Niue', 'Bahamas', 1],
 ['Niue', 'British Virgin Islands', 13],
 ['Niue', 'Panama', 236],
 ['Niue', 'Seychelles', 5],
 ['Norway', 'Bahamas', 1],
 ['Norway', 'Barbados', 2],
 ['Norway', 'Bermuda', 26],
 ['Norway', 'British Virgin Islands', 5],
 ['Norway', 'Cayman Islands', 6],
 ['Norway', 'Panama', 8],
 ['Oman', 'British Virgin Islands', 3],
 ['Oman', 'Cayman Islands', 1],
 ['Pakistan', 'Bahamas', 1],
 ['Pakistan', 'Bermuda', 1],
 ['Pakistan', 'British Virgin Islands', 1],
 ['Pakistan', 'Panama', 1],
 ['Panama', 'Bahamas', 1120],
 ['Panama', 'Barbados', 25],
 ['Panama', 'Belize', 40],
 ['Panama', 'British Anguilla', 278],
 ['Panama', 'British Virgin Islands', 4788],
 ['Panama', 'Cayman Islands', 4],
 ['Panama', 'Costa Rica', 5],
 ['Panama', 'Cyprus', 22],
 ['Panama', 'Hong Kong', 108],
 ['Panama', 'Malta', 1],
 ['Panama', 'Nevada', 82],
 ['Panama', 'New Zealand', 2],
 ['Panama', 'Niue', 1589],
 ['Panama', 'Samoa', 410],
 ['Panama', 'Seychelles', 1273],
 ['Panama', 'United Kingdom', 5],
 ['Panama', 'Uruguay', 7],
 ['Panama', 'Wyoming', 5],
 ['Paraguay', 'Bahamas', 10],
 ['Paraguay', 'British Virgin Islands', 27],
 ['Paraguay', 'Nevada', 5],
 ['Paraguay', 'Niue', 1],
 ['Paraguay', 'Panama', 84],
 ['Peru', 'Bahamas', 2],
 ['Peru', 'Barbados', 1],
 ['Peru', 'Bermuda', 1],
 ['Peru', 'British Virgin Islands', 10],
 ['Peru', 'Cyprus', 1],
 ['Peru', 'Hong Kong', 1],
 ['Peru', 'Isle of Man', 1],
 ['Peru', 'New Zealand', 1],
 ['Peru', 'Niue', 1],
 ['Peru', 'Panama', 35],
 ['Peru', 'Seychelles', 1],
 ['Philippines', 'Bahamas', 2],
 ['Philippines', 'Bermuda', 9],
 ['Philippines', 'British Anguilla', 1],
 ['Philippines', 'British Virgin Islands', 18],
 ['Philippines', 'Cayman Islands', 2],
 ['Philippines', 'Niue', 10],
 ['Philippines', 'Panama', 9],
 ['Philippines', 'Seychelles', 4],
 ['Poland', 'Bahamas', 58],
 ['Poland', 'British Virgin Islands', 28],
 ['Poland', 'Niue', 40],
 ['Poland', 'Panama', 4],
 ['Poland', 'Samoa', 1],
 ['Poland', 'Seychelles', 26],
 ['Poland', 'Wyoming', 1],
 ['Portugal', 'Bahamas', 44],
 ['Portugal', 'Barbados', 1],
 ['Portugal', 'Bermuda', 2],
 ['Portugal', 'British Virgin Islands', 62],
 ['Portugal', 'Cayman Islands', 1],
 ['Portugal', 'Cyprus', 5],
 ['Portugal', 'Hong Kong', 1],
 ['Portugal', 'Nevada', 2],
 ['Portugal', 'Niue', 56],
 ['Portugal', 'Panama', 63],
 ['Portugal', 'Samoa', 10],
 ['Puerto Rico', 'Barbados', 1],
 ['Puerto Rico', 'Panama', 1],
 ['Qatar', 'Bahamas', 4],
 ['Qatar', 'Bermuda', 1],
 ['Qatar', 'British Virgin Islands', 10],
 ['Qatar', 'Isle of Man', 5],
 ['Qatar', 'Panama', 12],
 ['Qatar', 'Seychelles', 1],
 ['Romania', 'British Anguilla', 2],
 ['Romania', 'British Virgin Islands', 2],
 ['Romania', 'Malta', 1],
 ['Romania', 'Panama', 2],
 ['Russia', 'Bahamas', 1335],
 ['Russia', 'Belize', 3],
 ['Russia', 'Bermuda', 25],
 ['Russia', 'British Anguilla', 73],
 ['Russia', 'British Virgin Islands', 2311],
 ['Russia', 'Cayman Islands', 5],
 ['Russia', 'Hong Kong', 3],
 ['Russia', 'Niue', 366],
 ['Russia', 'Panama', 94],
 ['Russia', 'Saint Kitts and Nevis', 1],
 ['Russia', 'Samoa', 5],
 ['Russia', 'Seychelles', 21],
 ['Russia', 'Uruguay', 2],
 ['Saint Kitts and Nevis', 'Bahamas', 9],
 ['Saint Kitts and Nevis', 'Barbados', 6],
 ['Saint Kitts and Nevis', 'British Virgin Islands', 26],
 ['Saint Kitts and Nevis', 'Cayman Islands', 1],
 ['Saint Kitts and Nevis', 'Cook Islands', 1],
 ['Saint Kitts and Nevis', 'Hong Kong', 14],
 ['Saint Kitts and Nevis', 'Mauritius', 1],
 ['Saint Kitts and Nevis', 'Nevada', 4],
 ['Saint Kitts and Nevis', 'Niue', 2],
 ['Saint Kitts and Nevis', 'Panama', 117],
 ['Saint Kitts and Nevis', 'Seychelles', 17],
 ['Saint Lucia', 'Barbados', 115],
 ['Saint Lucia', 'British Virgin Islands', 4],
 ['Saint Lucia', 'Panama', 1],
 ['Saint Lucia', 'Saint Lucia', 4],
 ['Saint Martin (French part)', 'Barbados', 1],
 ['Saint Vincent and the Grenadines', 'Barbados', 13],
 ['Saint Vincent and the Grenadines', 'British Virgin Islands', 3],
 ['Saint Vincent and the Grenadines', 'Panama', 3],
 ['Samoa', 'Bahamas', 121],
 ['Samoa', 'Bermuda', 1],
 ['Samoa', 'British Anguilla', 2],
 ['Samoa', 'British Virgin Islands', 250],
 ['Samoa', 'Cayman Islands', 2],
 ['Samoa', 'Cook Islands', 1],
 ['Samoa', 'Hong Kong', 16],
 ['Samoa', 'Niue', 78],
 ['Samoa', 'Panama', 160],
 ['Samoa', 'Seychelles', 850],
 ['Samoa', 'United Kingdom', 1],
 ['Saudi Arabia', 'Bahamas', 4],
 ['Saudi Arabia', 'Bermuda', 26],
 ['Saudi Arabia', 'British Virgin Islands', 76],
 ['Saudi Arabia', 'Cayman Islands', 2],
 ['Saudi Arabia', 'Niue', 3],
 ['Saudi Arabia', 'Panama', 24],
 ['Saudi Arabia', 'Seychelles', 1],
 ['Senegal', 'Panama', 1],
 ['Senegal', 'Seychelles', 2],
 ['Seychelles', 'Bahamas', 67],
 ['Seychelles', 'Barbados', 3],
 ['Seychelles', 'Belize', 1],
 ['Seychelles', 'British Anguilla', 2],
 ['Seychelles', 'British Virgin Islands', 161],
 ['Seychelles', 'Cayman Islands', 2],
 ['Seychelles', 'Panama', 83],
 ['Seychelles', 'Saint Kitts and Nevis', 2],
 ['Seychelles', 'Samoa', 14],
 ['Sierra Leone', 'Bermuda', 3],
 ['Singapore', 'Bahamas', 93],
 ['Singapore', 'Barbados', 1],
 ['Singapore', 'Belize', 1],
 ['Singapore', 'Bermuda', 96],
 ['Singapore', 'British Anguilla', 15],
 ['Singapore', 'British Virgin Islands', 4034],
 ['Singapore', 'Cayman Islands', 34],
 ['Singapore', 'Cook Islands', 48],
 ['Singapore', 'Hong Kong', 7],
 ['Singapore', 'Isle of Man', 2],
 ['Singapore', 'Labuan', 6],
 ['Singapore', 'Malaysia', 5],
 ['Singapore', 'Mauritius', 4],
 ['Singapore', 'Niue', 98],
 ['Singapore', 'Panama', 68],
 ['Singapore', 'Saint Lucia', 1],
 ['Singapore', 'Samoa', 122],
 ['Singapore', 'Seychelles', 127],
 ['Singapore', 'United Kingdom', 1],
 ['Singapore', 'Vanuatu', 1],
 ['Sint Maarten (Dutch part)', 'Panama', 1],
 ['Slovakia', 'Panama', 1],
 ['Slovenia', 'Bahamas', 3],
 ['Slovenia', 'British Anguilla', 16],
 ['Slovenia', 'British Virgin Islands', 1],
 ['Slovenia', 'Panama', 1],
 ['South Africa', 'Bahamas', 8],
 ['South Africa', 'Bermuda', 2],
 ['South Africa', 'British Virgin Islands', 61],
 ['South Africa', 'Cayman Islands', 4],
 ['South Africa', 'Isle of Man', 4],
 ['South Africa', 'Jersey', 1],
 ['South Africa', 'Niue', 7],
 ['South Africa', 'Panama', 33],
 ['South Korea', 'Barbados', 1],
 ...]

d = collections.defaultdict(int)
for n1, n2, v in df_jur_grouped_list:
    d[min(n1, n2), max(n1, n2)] += v
result = [[k[0], k[1], v] for k, v in d.items()]

df_net = pd.DataFrame(result)
df_net

	0	1	2
0	Albania	Bahamas	2
1	Andorra	Bahamas	26
2	Andorra	British Virgin Islands	39
3	Andorra	Cayman Islands	1
4	Andorra	Costa Rica	3
...	...	...	...
1125	Panama	Vietnam	1
1126	Panama	Yemen	1
1127	British Virgin Islands	Zambia	2
1128	British Virgin Islands	Zimbabwe	5
1129	Panama	Zimbabwe	3

1130 rows × 3 columns

df_net = df_net.rename(columns = {0:'node_1', 1:'node_2', 2: 'weight'})
df_net

	node_1	node_2	weight
0	Albania	Bahamas	2
1	Andorra	Bahamas	26
2	Andorra	British Virgin Islands	39
3	Andorra	Cayman Islands	1
4	Andorra	Costa Rica	3
...	...	...	...
1125	Panama	Vietnam	1
1126	Panama	Yemen	1
1127	British Virgin Islands	Zambia	2
1128	British Virgin Islands	Zimbabwe	5
1129	Panama	Zimbabwe	3

1130 rows × 3 columns

#df_net.to_csv('undirected_weighted_grouped.csv', index = False)

df_net.describe()

	weight
count	1130.000000
mean	191.645133
std	1208.036677
min	1.000000
25%	2.000000
50%	5.000000
75%	31.750000
max	27501.000000

Create Graph

g = ig.Graph.TupleList(df_net.itertuples(index=False), directed=False, edge_attrs={'weight': "weight"})

ig.plot(g)

g.diameter()

4

print( "Clustering Coefficient: ", g.transitivity_undirected())

Clustering Coefficient:  0.24672505151604357

np.mean(g.degree())

12.988505747126437

np.mean(g.strength(weights = 'weight'))

2489.183908045977

Metrics

def get_diameter(community):
    
    diameters = []
    
    for i in range(len(community.subgraphs())):
    
        diameters.append(community.subgraphs()[i].diameter())

    return (diameters)

def get_transitivity(community):
    
    transitivity = []
    
    for i in range(len(community.subgraphs())):
    
        transitivity.append(community.subgraphs()[i].transitivity_undirected())

    return (transitivity)

def get_avg_degree(community):
    
    degree = []
    
    for i in range(len(community.subgraphs())):
    
        degree.append(np.mean(community.subgraphs()[i].degree()))

    return degree

def get_avg_weighted_degree(community):
    
    weighted_degree = []
    
    for i in range(len(community.subgraphs())):
    
        weighted_degree.append(np.mean(community.subgraphs()[i].strength(weights = 'weight')))

    return weighted_degree

Community Detection

Louvain Community Detection

comm_louvain = g.community_multilevel(weights = 'weight')

ig.plot(comm_louvain, mark_groups = True, layout="drl")

modularity = g.modularity(comm_louvain, weights='weight')
print(modularity)

0.16250051107594074

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_louvain))
print('average degree: ', get_avg_degree(comm_louvain))
print('transitivity: ', get_transitivity(comm_louvain))
print('diameter: ', get_diameter(comm_louvain))

average weighted degree:  [264.3529411764706, 1489.276595744681, 2322.206896551724, 345.68, 422.0]
average degree:  [2.235294117647059, 4.638297872340425, 4.241379310344827, 5.16, 1.0]
transitivity:  [0.04838709677419355, 0.18663594470046083, 0.18903318903318903, 0.22540250447227192, nan]
diameter:  [2, 3, 3, 4, 1]

df_louvain = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_louvain.membership } )
df_louvain

	name	community
0	Albania	0
1	Bahamas	0
2	Andorra	1
3	British Virgin Islands	2
4	Cayman Islands	3
...	...	...
169	Venezuela	1
170	Vietnam	3
171	Yemen	1
172	Zambia	2
173	Zimbabwe	2

174 rows × 2 columns

df_louvain.describe()

	community
count	174.000000
mean	1.844828
std	0.987817
min	0.000000
25%	1.000000
50%	2.000000
75%	3.000000
max	4.000000

df_louvain[df_louvain['community'] == 0]

	name	community
0	Albania	0
1	Bahamas	0
27	Azerbaijan	0
28	Cyprus	0
30	Netherlands Antilles	0
31	Wyoming	0
42	Netherlands	0
60	US Virgin Islands	0
71	Denmark	0
80	Finland	0
85	Ghana	0
129	Nigeria	0
136	Poland	0
141	Russia	0
157	Sweden	0
159	Syria	0
166	Ukraine	0

df_louvain[df_louvain['community'] == 1].head(99)

	name	community
2	Andorra	1
5	Costa Rica	1
7	Nevada	1
8	Niue	1
9	Panama	1
13	Uruguay	1
16	Antigua and Barbuda	1
19	Argentina	1
36	Belgium	1
37	Belize	1
46	Bolivia	1
48	Brazil	1
57	Luxembourg	1
63	Chile	1
65	Colombia	1
70	Côte d'Ivoire	1
72	Djibouti	1
73	Dominica	1
74	Dominican Republic	1
75	Ecuador	1
76	Egypt	1
77	El Salvador	1
84	Germany	1
86	Greece	1
88	Guatemala	1
90	Haiti	1
91	Honduras	1
98	Iran	1
107	Kuwait	1
112	Libya	1
113	Lithuania	1
119	Mexico	1
121	Monaco	1
127	Nauru	1
128	Nicaragua	1
133	Paraguay	1
134	Peru	1
137	Portugal	1
138	Puerto Rico	1
139	Qatar	1
148	Sint Maarten (Dutch part)	1
149	Slovakia	1
153	Spain	1
155	Sudan	1
158	Switzerland	1
169	Venezuela	1
171	Yemen	1

df_louvain[df_louvain['community'] == 2]

	name	community
3	British Virgin Islands	2
6	Hong Kong	2
12	United Kingdom	2
14	Angola	2
17	Barbados	2
21	Aruba	2
25	Isle of Man	2
29	Malta	2
35	Belarus	2
38	Mauritius	2
39	Jersey	2
40	Liberia	2
41	Liechtenstein	2
43	Singapore	2
44	Turks and Caicos Islands	2
50	Brunei	2
54	Gibraltar	2
56	Guernsey	2
61	Central African Republic	2
62	Chad	2
66	Croatia	2
67	Cuba	2
69	Czech Republic	2
78	Estonia	2
82	French Guiana	2
87	Guam	2
89	Guyana	2
95	Iceland	2
97	Indonesia	2
99	Ireland	2
102	Jamaica	2
104	Jordan	2
105	Kazakhstan	2
106	Kenya	2
108	Kyrgyzstan	2
109	Latvia	2
114	Macao	2
116	Malawi	2
118	Mali	2
120	Moldova	2
122	Mongolia	2
123	Montenegro	2
131	Oman	2
142	Saint Lucia	2
143	Saint Martin (French part)	2
147	Vanuatu	2
151	South Africa	2
154	Sri Lanka	2
156	Suriname	2
160	Taiwan	2
161	Tanzania	2
162	Trinidad and Tobago	2
163	Tunisia	2
164	Turkey	2
165	Uganda	2
168	Uzbekistan	2
172	Zambia	2
173	Zimbabwe	2

df_louvain[df_louvain['community'] == 3]

	name	community
4	Cayman Islands	3
10	Samoa	3
11	Seychelles	3
15	Bermuda	3
18	Saint Kitts and Nevis	3
20	New Zealand	3
22	British Anguilla	3
23	Australia	3
24	Cook Islands	3
26	Austria	3
32	Bahrain	3
33	United Arab Emirates	3
34	Bangladesh	3
45	United States	3
47	Botswana	3
49	State of Delaware	3
51	Bulgaria	3
52	Cameroon	3
53	Canada	3
55	Grenada	3
58	Marshall Islands	3
59	Saint Vincent and the Grenadines	3
64	China	3
68	Curaçao	3
79	Fiji	3
81	France	3
83	Georgia	3
93	Thailand	3
94	Hungary	3
96	India	3
100	Israel	3
101	Italy	3
103	Japan	3
110	Lebanon	3
111	Lesotho	3
115	Macedonia	3
124	Morocco	3
125	Mozambique	3
126	Namibia	3
130	Norway	3
132	Pakistan	3
135	Philippines	3
140	Romania	3
144	Saudi Arabia	3
145	Senegal	3
146	Sierra Leone	3
150	Slovenia	3
152	South Korea	3
167	Dubai	3
170	Vietnam	3

df_louvain[df_louvain['community'] == 4]

	name	community
92	Labuan	4
117	Malaysia	4

# Define colors used for visualization
colors = ['blue', 'yellow', 'green','purple', 'pink', 'orange', 'lightcoral', 'plum', 'darkgreen', 'darkblue', 'rosybrown',
         'crimson', 'steelblue', 'khaki', 'moccasin', 'orangered', 'fuchsia', 'thistle', 'salmon', 'chocolate', 'lightseagreen',
         'springgreen', 'darkseagreen', 'deeppink', 'mediumvioletred','olive']

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_louvain.membership)):
    vertex_colors.append(colors[comm_louvain.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_images/louvain_communities.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_images/louvain_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_louvain.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_louvain.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_louvain.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_louvain.subgraphs()[i],'jur_images/louvain_subgraph' + str(i) + '.png' ,vertex_label = comm_louvain.subgraphs()[i].vs['name'],**visual_style  )
    
    
    

# # Set vertex colours

# visual_style["bbox"] = (300,300)
# visual_style["margin"] = 17
# visual_style["vertex_color"] = 'orange'

# # Set vertex label size
# visual_style["vertex_label_size"] = 15

# # Set the layout
# my_layout = comm_louvain.cluster_graph().layout_fruchterman_reingold()
# visual_style["layout"] = my_layout

# ig.plot(comm_louvain.cluster_graph(),'jur_images/test_cluster_graph.png' ,vertex_label = range(comm_louvain.cluster_graph().vcount()),**visual_style  )

Label Propogation Community Detection

comm_lp= g.community_label_propagation(weights = 'weight')

ig.plot(comm_lp, mark_groups = True, layout="drl")

modularity = g.modularity(comm_lp, weights='weight')
print(modularity)

0.003888080328497982

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_lp))
print('average degree: ', get_avg_degree(comm_lp))
print('transitivity: ', get_transitivity(comm_lp))
print('diameter: ', get_diameter(comm_lp))

average weighted degree:  [2512.2093023255816, 422.0]
average degree:  [12.965116279069768, 1.0]
transitivity:  [0.24856837085296934, nan]
diameter:  [4, 1]

Leading Eigenvector Community Detection

comm_leading_eigenvector= g.community_leading_eigenvector(weights = 'weight')

ig.plot(comm_leading_eigenvector, mark_groups = True, layout="drl")

modularity = g.modularity(comm_leading_eigenvector, weights='weight')
print(modularity)

0.1323189128776745

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_leading_eigenvector))
print('average degree: ', get_avg_degree(comm_leading_eigenvector))
print('transitivity: ', get_transitivity(comm_leading_eigenvector))
print('diameter: ', get_diameter(comm_leading_eigenvector))

average weighted degree:  [896.2156862745098, 2705.4444444444443]
average degree:  [5.098039215686274, 8.444444444444445]
transitivity:  [0.1493288590604027, 0.33534222987516143]
diameter:  [4, 2]

df_leading_eigenvector = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_leading_eigenvector.membership } )
df_leading_eigenvector

	name	community
0	Albania	0
1	Bahamas	0
2	Andorra	0
3	British Virgin Islands	1
4	Cayman Islands	1
...	...	...
169	Venezuela	0
170	Vietnam	1
171	Yemen	0
172	Zambia	1
173	Zimbabwe	0

174 rows × 2 columns

df_leading_eigenvector.describe()

	community
count	174.000000
mean	0.413793
std	0.493934
min	0.000000
25%	0.000000
50%	0.000000
75%	1.000000
max	1.000000

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_leading_eigenvector.membership)):
    vertex_colors.append(colors[comm_leading_eigenvector.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_images/communities_leading_eigenvector.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_images/leading_vector_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_leading_eigenvector.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_leading_eigenvector.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_leading_eigenvector.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_leading_eigenvector.subgraphs()[i],'jur_images/leading_eigenvector_subgraph' + str(i) + '.png' ,vertex_label = comm_leading_eigenvector.subgraphs()[i].vs['name'],**visual_style  )
    

Leiden Community Detection

comm_leiden = g.community_leiden(weights = 'weight')

ig.plot(comm_leiden, mark_groups = True, layout="drl")

modularity = g.modularity(comm_leiden, weights='weight')
print(modularity)

0.0038620588629392967

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_leiden))
print('average degree: ', get_avg_degree(comm_leiden))
print('transitivity: ', get_transitivity(comm_leiden))
print('diameter: ', get_diameter(comm_leiden))

average weighted degree:  [0.0, 5713.626666666667, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 422.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
average degree:  [0.0, 20.586666666666666, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
transitivity:  [nan, 0.4242458736482641, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
diameter:  [0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

df_leiden = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_leiden.membership } )
df_leiden

	name	community
0	Albania	0
1	Bahamas	1
2	Andorra	1
3	British Virgin Islands	1
4	Cayman Islands	1
...	...	...
169	Venezuela	1
170	Vietnam	95
171	Yemen	96
172	Zambia	97
173	Zimbabwe	98

174 rows × 2 columns

df_leiden.describe()

	community
count	174.000000
mean	28.551724
std	32.132946
min	0.000000
25%	1.000000
50%	12.500000
75%	54.750000
max	98.000000

## no visualisation as most communities are individual?

Optimal Modularity Community Detection

# #SLOW

# comm_opt_modularity = g.community_optimal_modularity(weights = 'weight')

# ig.plot(comm_opt_modularity, mark_groups = True, layout="drl")

# modularity = g.modularity(comm_opt_modularity, weights='weight')
# print(modularity)

# df_opt_modularity = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_opt_modularity.membership } )
# df_opt_modularity

# df_opt_modularity.describe

# ##plot community
# visual_style = {}

# vertex_colors = []

# for i in range(len(df_opt_modularity.membership)):
#     vertex_colors.append(colors[df_opt_modularity.membership[i]])
    
# # Set bbox and margin
# visual_style["bbox"] = (5000,5000)
# visual_style["margin"] = 17

# # Set vertex colours
# visual_style["vertex_color"] = vertex_colors

# # Set edge width
# visual_style["edge_width"] = np.log(g.es['weight']) + 1

# # Set edge color
# visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# # Set vertex size
# visual_style["vertex_size"] = 15

# # Set vertex label size
# visual_style["vertex_label_size"] = 20

# # Set vertex label color
# visual_style["vertex_label_color"] = 'red'

# # Don't curve the edges
# visual_style["edge_curved"] = False

# # Set the layout
# my_layout = g.layout_fruchterman_reingold()
# visual_style["layout"] = my_layout

# # Plot the graph
# ig.plot(g,'jur_images/communities_optimal_modularity.png', vertex_label = g.vs['name'] , **visual_style)

# # Set the layout
# my_layout = g.layout_circle()
# visual_style["layout"] = my_layout

# # Plot the graph
# ig.plot(g,'jur_images/optimal_modularity_circular.png', vertex_label = g.vs['name'] , **visual_style)

# for i in range(len(optimal_modularity.subgraphs())):
    
#     visual_style["bbox"] = (2000,2000)
#     visual_style["margin"] = 17
    
#     # Set vertex colours
#     visual_style["vertex_color"] = 'green'

#     # Set edge width
#     visual_style["edge_width"] = np.log(optimal_modularity.subgraphs()[i].es['weight']) + 1
#     # Set the layout
#     my_layout = optimal_modularity_circular.subgraphs()[i].layout_fruchterman_reingold()
#     visual_style["layout"] = my_layout

#     ig.plot(optimal_modularity_circular.subgraphs()[i],'jur_images/optimal_modularity_subgraph' + str(i) + '.png' ,vertex_label = optimal_modularity.subgraphs()[i].vs['name'],**visual_style  )
    

Spinglass Community Detection

comm_spinglass = g.community_spinglass(weights = 'weight')

ig.plot(comm_spinglass, mark_groups = True, layout="drl")

modularity = g.modularity(comm_spinglass, weights='weight')
print(modularity)

0.1647219256291074

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_spinglass))
print('average degree: ', get_avg_degree(comm_spinglass))
print('transitivity: ', get_transitivity(comm_spinglass))
print('diameter: ', get_diameter(comm_spinglass))

average weighted degree:  [316.7142857142857, 424.4166666666667, 1589.4545454545455, 422.0, 2843.695652173913, 146.36363636363637]
average degree:  [2.0, 3.0833333333333335, 4.7727272727272725, 1.0, 3.608695652173913, 4.636363636363637]
transitivity:  [0.0375, 0.20454545454545456, 0.20277953253316489, nan, 0.15916398713826366, 0.19794721407624633]
diameter:  [2, 3, 3, 1, 3, 4]

df_spinglass = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_spinglass.membership } )
df_spinglass

	name	community
0	Albania	0
1	Bahamas	0
2	Andorra	2
3	British Virgin Islands	4
4	Cayman Islands	5
...	...	...
169	Venezuela	2
170	Vietnam	5
171	Yemen	2
172	Zambia	4
173	Zimbabwe	4

174 rows × 2 columns

df_spinglass.describe()

	community
count	174.000000
mean	3.000000
std	1.679526
min	0.000000
25%	2.000000
50%	4.000000
75%	4.750000
max	5.000000

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_spinglass.membership)):
    vertex_colors.append(colors[comm_spinglass.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_images/communities_spinglass.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_images/spinglass_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_spinglass.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_spinglass.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_spinglass.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_spinglass.subgraphs()[i],'jur_images/spinglass_subgraph' + str(i) + '.png' ,vertex_label = comm_spinglass.subgraphs()[i].vs['name'],**visual_style  )
    

Walktrap Community Detection

walktrap = g.community_walktrap(weights = 'weight', steps = 2)

comm_walktrap = walktrap.as_clustering()

ig.plot(comm_walktrap, mark_groups = True, layout="drl")

modularity = g.modularity(comm_walktrap, weights='weight')
print(modularity)

0.02637219748719517

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_walktrap))
print('average degree: ', get_avg_degree(comm_walktrap))
print('transitivity: ', get_transitivity(comm_walktrap))
print('diameter: ', get_diameter(comm_walktrap))

average weighted degree:  [0.0, 5650.112676056338, 193.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
average degree:  [0.0, 16.309859154929576, 5.4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
transitivity:  [nan, 0.37092458085303914, 0.26413043478260867, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
diameter:  [0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

df_walktrap = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_walktrap.membership } )
df_walktrap

	name	community
0	Albania	0
1	Bahamas	1
2	Andorra	1
3	British Virgin Islands	1
4	Cayman Islands	2
...	...	...
169	Venezuela	1
170	Vietnam	2
171	Yemen	72
172	Zambia	73
173	Zimbabwe	74

174 rows × 2 columns

df_walktrap.describe()

	community
count	174.000000
mean	16.683908
std	22.754992
min	0.000000
25%	1.000000
50%	2.000000
75%	30.750000
max	74.000000

## no visualisation since too many communities, mostly individual

Infomap Community Detection

comm_infomap = g.community_infomap(edge_weights = 'weight')

ig.plot(comm_infomap, mark_groups = True, layout="drl")

modularity = g.modularity(comm_infomap, weights='weight')
print(modularity)

0.003888080328497982

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_infomap))
print('average degree: ', get_avg_degree(comm_infomap))
print('transitivity: ', get_transitivity(comm_infomap))
print('diameter: ', get_diameter(comm_infomap))

average weighted degree:  [2512.2093023255816, 422.0]
average degree:  [12.965116279069768, 1.0]
transitivity:  [0.24856837085296934, nan]
diameter:  [4, 1]

df_infomap = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_infomap.membership } )
df_infomap

	name	community
0	Albania	0
1	Bahamas	0
2	Andorra	0
3	British Virgin Islands	0
4	Cayman Islands	0
...	...	...
169	Venezuela	0
170	Vietnam	0
171	Yemen	0
172	Zambia	0
173	Zimbabwe	0

174 rows × 2 columns

## no visualisation since no community is detected

Fast Greedy Community Detection

fg = g.community_fastgreedy(weights = 'weight')

comm_fastgreedy = fg.as_clustering()

ig.plot(comm_fastgreedy, mark_groups = True, layout="drl")

modularity = g.modularity(comm_fastgreedy, weights='weight')
print(modularity)

0.1628777314853962

#get metrics for each community

print('average weighted degree: ', get_avg_weighted_degree(comm_fastgreedy))
print('average degree: ', get_avg_degree(comm_fastgreedy))
print('transitivity: ', get_transitivity(comm_fastgreedy))
print('diameter: ', get_diameter(comm_fastgreedy))

average weighted degree:  [334.6666666666667, 1591.953488372093, 2881.063829787234, 182.7111111111111, 269.0, 422.0]
average degree:  [2.1333333333333333, 4.0, 3.6595744680851063, 4.933333333333334, 2.3636363636363638, 1.0]
transitivity:  [0.07142857142857142, 0.16792738275340394, 0.1574144486692015, 0.22103148024112526, 0.07407407407407407, nan]
diameter:  [3, 2, 3, 4, 3, 1]

df_fastgreedy = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_fastgreedy.membership } )
df_fastgreedy

	name	community
0	Albania	0
1	Bahamas	0
2	Andorra	1
3	British Virgin Islands	2
4	Cayman Islands	3
...	...	...
169	Venezuela	1
170	Vietnam	3
171	Yemen	1
172	Zambia	2
173	Zimbabwe	2

174 rows × 2 columns

df_fastgreedy.describe()

	community
count	174.000000
mean	2.126437
std	1.205043
min	0.000000
25%	1.000000
50%	2.000000
75%	3.000000
max	5.000000

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_fastgreedy.membership)):
    vertex_colors.append(colors[comm_fastgreedy.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_images/communities_fastgreedy.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_images/fastgreedy_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_fastgreedy.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_fastgreedy.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_fastgreedy.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_fastgreedy.subgraphs()[i],'jur_images/fastgreedy_subgraph' + str(i) + '.png' ,vertex_label = comm_fastgreedy.subgraphs()[i].vs['name'],**visual_style  )